The present invention concerns method for sensibly real-time deformable fusion of a source multi-dimensional image and a target multi-dimensional image of an object.
The field of the invention is the field of multi-dimensional image fusion, the images being either of same modalities or of different modalities of image acquisition.
In particular, the invention concerns three-dimensional (3D-3D) image fusion.
The 3D-3D mono-modal and multi-modal image fusion finds applications in multiple medical procedures, such as biomedical image analysis, adaptive radiotherapy treatment, spinal surgery, hip replacement, neuro-interventions and aortic stenting in particular when “real-time” performance is considered.
Various modalities include computed tomography (CT), radiography, magnetic resonance, ultrasound.
Deformable fusion, which is one of the greatest challenges in medical imaging, seeks the estimation of a smooth deformation field that optimizes a similarity criterion between the source image and the target image.
In the multi-modal case, the definition of an appropriate similarity metric between images is particularly difficult, since intensity variations between a source image and a target image are unknown and may be particularly large and highly non-linear.
Moreover, deformable fusion is problematic because it is difficult to use known methods, such as statistical based methods, to estimate local densities of probability to define a local similarity criterion.
Modality specific methods have been proposed. Such methods rely on the segmentation of objects of interest, such as organs or tissues, in each modality, and therefore the quality of segmentation is key to their efficiency. Moreover, a prior knowledge of the physical laws that relate the intensities between modalities is necessary which diminishes the applicability of such a methods in a more generic setting.
Some known methods are based on defining an appropriate metric after training on training data sets. Therefore, these methods lack of generalization in terms of modalities to be handled as well as in terms of data/objets of interest not seen during training.
Once the similarity criterion has been appropriately defined, it has to be combined with smoothness penalty towards overcoming the ill-posedeness of the estimation problem. The importance of the smoothness constraint could have a tremendous impact on the quality of the obtained solution ranging from oversmoothing of the deformation (which is often equivalent with inability of capturing important organ deformations) to meaningless deformation models (under smoothing) where established correspondences do not have meaningful anatomical interpretations.
Last, but not least, the adoption/use of deformable multi-modal fusion methods in clinical setting is constrained by their computational complexity (running time). Despite tremendous progress made over the past decade in terms of computing resources, none of the existing state of the art methods meet close to real-time performance unless highly dedicated and considerably expensive architectures are considered. Towards addressing this need most of the effort was invested to take advantage of computing power with respect to existing methods and not developing new methods when once considered with modern parallel architectures could lead to near real-time performance.
An aim of the present invention is to provide an elastic/deformable fusion method that overcomes the cited drawbacks.